from statistics import mean

import numpy as np





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      0
 # Number of processes finished
 UpperCamelCase			  =      0
 # Displays the finished process.
 # If it is 0, the performance is completed if it is 1, before the performance.
 UpperCamelCase			  =      [0] * no_of_process
 # List to include calculation results
 UpperCamelCase			  =      [0] * no_of_process

 # Sort by arrival time.
 UpperCamelCase			  =      [burst_time[i] for i in np.argsort(UpperCamelCase_       )]
 UpperCamelCase			  =      [process_name[i] for i in np.argsort(UpperCamelCase_       )]
 arrival_time.sort()

 while no_of_process > finished_process_count:
  UpperCamelCase			  =      0
  while finished_process[i] == 1:
   i += 1
  if current_time < arrival_time[i]:
   UpperCamelCase			  =      arrival_time[i]

  UpperCamelCase			  =      0
  # Index showing the location of the process being performed
  UpperCamelCase			  =      0
  # Saves the current response ratio.
  UpperCamelCase			  =      0
  for i in range(0							,					UpperCamelCase_       ):
   if finished_process[i] == 0 and arrival_time[i] <= current_time:
    UpperCamelCase			  =      (burst_time[i] + (current_time - arrival_time[i])) / burst_time[
        i
    ]
   if response_ratio < temp:
    UpperCamelCase			  =      temp
    UpperCamelCase			  =      i

        # Calculate the turn around time
  UpperCamelCase			  =      current_time + burst_time[loc] - arrival_time[loc]
  current_time += burst_time[loc]
  # Indicates that the process has been performed.
  UpperCamelCase			  =      1
  # Increase finished_process_count by 1
  finished_process_count += 1

 return turn_around_time





def 			lowercase(	UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_							,					UpperCamelCase_       )		->	list:







 '''simple docstring'''
 UpperCamelCase			  =      [0] * no_of_process
 for i in range(0							,					UpperCamelCase_       ):
  UpperCamelCase			  =      turn_around_time[i] - burst_time[i]
 return waiting_time


if __name__ == "__main__":
     _SCREAMING_SNAKE_CASE 		=      5
     _SCREAMING_SNAKE_CASE 		=      ["""A""", """B""", """C""", """D""", """E"""]
     _SCREAMING_SNAKE_CASE 		=      [1, 2, 3, 4, 5]
     _SCREAMING_SNAKE_CASE 		=      [1, 2, 3, 4, 5]

     _SCREAMING_SNAKE_CASE 		=      calculate_turn_around_time(
         process_name, arrival_time, burst_time, no_of_process
     )
     _SCREAMING_SNAKE_CASE 		=      calculate_waiting_time(
         process_name, turn_around_time, burst_time, no_of_process
     )

     print("""Process name \tArrival time \tBurst time \tTurn around time \tWaiting time""")
     for i in range(0, no_of_process):
          print(
              F'''{process_name[i]}\t\t{arrival_time[i]}\t\t{burst_time[i]}\t\t'''
              F'''{turn_around_time[i]}\t\t\t{waiting_time[i]}'''
          )

     print(F'''average waiting time : {mean(waiting_time):.5f}''')
     print(F'''average turn around time : {mean(turn_around_time):.5f}''')

from collections import OrderedDict
from typing import Mapping

from packaging import version

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


_SCREAMING_SNAKE_CASE 		=      logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE 		=      {
    """sail/poolformer_s12""": """https://huggingface.co/sail/poolformer_s12/resolve/main/config.json""",
    # See all PoolFormer models at https://huggingface.co/models?filter=poolformer
}
class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				"""poolformer"""







   def __init__(     self						:						str   ,   lowerCamelCase_						:						Any=3   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Any=16   ,   lowerCamelCase_						:						Optional[Any]=3   ,   lowerCamelCase_						:						List[str]=4.0   ,   lowerCamelCase_						:						Tuple=[2, 2, 6, 2]   ,   lowerCamelCase_						:						Optional[Any]=[64, 128, 320, 512]   ,   lowerCamelCase_						:						Tuple=[7, 3, 3, 3]   ,   lowerCamelCase_						:						Optional[Any]=[4, 2, 2, 2]   ,   lowerCamelCase_						:						Dict=[2, 1, 1, 1]   ,   lowerCamelCase_						:						Tuple=4   ,   lowerCamelCase_						:						Optional[int]=0.0   ,   lowerCamelCase_						:						Union[str, Any]="gelu"   ,   lowerCamelCase_						:						Union[str, Any]=True   ,   lowerCamelCase_						:						Union[str, Any]=1E-5   ,   lowerCamelCase_						:						int=0.0_2   ,   **lowerCamelCase_						:						str   ,   ):





    """simple docstring"""


    UpperCamelCase			  =      num_channels
    UpperCamelCase			  =      patch_size
    UpperCamelCase			  =      stride
    UpperCamelCase			  =      padding
    UpperCamelCase			  =      pool_size
    UpperCamelCase			  =      hidden_sizes
    UpperCamelCase			  =      mlp_ratio
    UpperCamelCase			  =      depths
    UpperCamelCase			  =      patch_sizes
    UpperCamelCase			  =      strides
    UpperCamelCase			  =      num_encoder_blocks
    UpperCamelCase			  =      drop_path_rate
    UpperCamelCase			  =      hidden_act
    UpperCamelCase			  =      use_layer_scale
    UpperCamelCase			  =      layer_scale_init_value
    UpperCamelCase			  =      initializer_range
    super().__init__(**lowerCamelCase_				)





class SCREAMING_SNAKE_CASE_		(      __lowerCAmelCase		):
   __lowerCAmelCase           =				version.parse("""1.11"""		)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return OrderedDict(
        [
            ("""pixel_values""", {0: """batch""", 1: """num_channels""", 2: """height""", 3: """width"""}),
        ]				)







   @property
   def 						lowerCamelCase_	(     self						:						Any				):





    """simple docstring"""


    return 2E-3
